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Register a new userThe VIRMOS mask manufacturing tools: (b) Mask manufacturing and handling
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We describe the VIRMOS Mask Manufacturing Unit (MMU) configuration, composed of two units:the Mask Manufacturing Machine (with its Control Unit) and the Mask Handling Unit (inclusive of Control Unit, Storage Cabinets and robot for loading of the Instrument Cabinets). For both VIMOS and NIRMOS instruments, on the basis of orders received by the Mask Preparation Software (see paper (a) in same proceedings), the function of the MMU is to perform an off-line mask cutting and identification, followed by mask storing and subsequent filling of the Instrument Cabinets (IC). We describe the characteristics of the LPKF laser cutting machine and the work done to support the choice of this equipment. We also describe the remaining of the hardware configuration and the Mask Handling Software.
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The VIRMOS Consortium has the task to design and manufacture two spectrographs for ESO VLT, VIMOS (Visible Multi-Object Spectrograph) and NIRMOS (Near Infrared Multi-Object Spectrograph). This paper describes how the Mask Manufacturing Unit (MMU), which cuts the slit masks to be used with both instruments, meets the scientific requirements and manages the storage and the insertion of the masks into the instrument. The components and the software of the two main parts of the MMU, the Mask Manufacturing Machine and the Mask Handling System, are illustrated together with the mask material and with the slit properties. Slit positioning is accurate within 15 micron, equivalent to 0.03 arcsec on the sky, while the slit edge roughness has an rms on the order of 0.03 pixels on scales of a slit 5 arcsec long and of 0.01 pixels on the pixel scale (0.205 arcsec). The MMU has been successfully installed during July/August 2000 at the Paranal Observatory and is now operational for spectroscopic mask cutting, compliant with the requested specifications.
Phase-mask coronagraphs are known to provide high contrast imaging capabilities while preserving a small inner working angle, which allows searching for exoplanets or circumstellar disks with smaller telescopes or at longer wavelengths. The AGPM (Annular Groove Phase Mask, Mawet et al. 2005) is an optical vectorial vortex coronagraph (or vector vortex) induced by a rotationally symmetric subwavelength grating (i.e. with a period smaller than {lambda}/n, {lambda} being the observed wavelength and n the refractive index of the grating substrate). In this paper, we present our first mid- infrared AGPM prototypes imprinted on a diamond substrate. We firstly give an extrapolation of the expected coronagraph performances in the N-band (~10 {mu}m), and prospects for down-scaling the technology to the most wanted L- band (~3.5 {mu}m). We then present the manufacturing and measurement results, using diamond-optimized microfabrication techniques such as nano-imprint lithography (NIL) and reactive ion etching (RIE). Finally, the subwavelength grating profile metrology combines surface metrology (scanning electron microscopy, atomic force microscopy, white light interferometry) with diffractometry on an optical polarimetric bench and cross correlation with theoretical simulations using rigorous coupled wave analysis (RCWA).
Labor productivity was studied at the microscopic level in terms of distributions based on individual firm financial data from Japan and the US. A power-law distribution in terms of firms and sector productivity was found in both countries data. The labor productivities were not equal for nation and sectors, in contrast to the prevailing view in the field of economics. It was found that the low productivity of the Japanese non-manufacturing sector reported in macro-economic studies was due to the low productivity of small firms.
Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. Therefore, analysis of multiple security data can provide comprehensive and system-wide anomaly detection in industrial networks. In this paper, we propose an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWAT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors.
Binary grid mask representation is broadly used in instance segmentation. A representative instantiation is Mask R-CNN which predicts masks on a $28times 28$ binary grid. Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity. In this paper, we propose a new mask representation by applying the discrete cosine transform(DCT) to encode the high-resolution binary grid mask into a compact vector. Our method, termed DCT-Mask, could be easily integrated into most pixel-based instance segmentation methods. Without any bells and whistles, DCT-Mask yields significant gains on different frameworks, backbones, datasets, and training schedules. It does not require any pre-processing or pre-training, and almost no harm to the running speed. Especially, for higher-quality annotations and more complex backbones, our method has a greater improvement. Moreover, we analyze the performance of our method from the perspective of the quality of mask representation. The main reason why DCT-Mask works well is that it obtains a high-quality mask representation with low complexity. Code is available at https://github.com/aliyun/DCT-Mask.git.
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